SAFAARI (Schema-Aware Framework for Accelerated Advertiser Response Intelligence), a multi-agent framework that addresses the critical bottleneck of schema linking in Natural Language to SQL (NL-to-SQL) systems through specialized content, metadata, and orchestration agents, is presented.
Abstract
The evolution of customer support systems is rapidly advancing with agentic chatbots, yet these systems face significant limitations when accessing enterprise data without predefined API endpoints. This paper presents SAFAARI (Schema-Aware Framework for Accelerated Advertiser Response Intelligence), a multi-agent framework that addresses the critical bottleneck of schema linking in Natural Language to SQL (NL-to-SQL) systems through specialized content, metadata, and orchestration agents. We also introduce SEAL (Schema Evaluation and Accuracy in Language-to-SQL), a novel composite metric that holistically evaluates system performance while penalizing inconsistent results. Through systematic experimentation with five feature set configurations, SAFAARI achieves an 81.66% SEAL score (6.65% improvement over baseline), with notable gains in datapoint accuracy (5.51%) and schema-linking precision (4.69%). The framework's effectiveness is validated through human-in-the-loop evaluation with domain experts, which proves its adaptability across diverse support domains. By automating the labor-intensive process of schema linking and query generation, our framework demonstrates 8x reduction in development time while maintaining high accuracy. The solution streamlines API development and enhances self-service capabilities, particularly benefiting customer support enterprises with complex data ecosystems.
It is argued that the key challenges are bridging the gap between personal and organizational memory, building effective agentic retrieval beyond naive RAG, and enabling organizations to evaluate their agents against their own data through self-serving evals.
Till Döhmen, MotherDuckNetherlands, Jacob Matson et al.· 0 citations
Extract-Transform-Load (ETL) processes are essential for modern data-driven enterprises, enabling strategic business decision-making by integrating heterogeneous data sources into decision-support information systems. The reliability of ETL pipelines hinges on semantically correct schema matching between source schemas and the ETL-defined target schema. However, this task is complicated by schema heterogeneity and limited data access due to confidentiality constraints. Although many systems now leverage machine learning and large language models (LLMs), they still largely rely on manual intervention and large amounts of labeled data, resulting in low accuracy and limited adaptability to sensitive contexts. This paper proposes “E-AGMatch”, an automated hybrid schema matching approach powered by an open-source LLM. Its reasoning is guided by an agentic prompt built through advanced prompt engineering techniques and anchored with deterministic tools for scoring, voting, and verification. This design enables proactive schema correspondence generation while mitigating the typical LLM stochastic variability and ensuring result validation. A prototype evaluated on Purchase Order schema metadata showed promising performance, enhancing automation and reducing the requirement to access data.
Ahlem Ben Younes, Chaima Kachroud, Laila Ben Ayed et al.· Annual International Compute...· 0 citations
DataSpace, a benchmark in which data agents produce verifiable tabular results from task-local heterogeneous workspaces, is introduced and key challenges for improving data-agent reliability are identified.
Boyan Li, Zhuowen Liang, Yupeng Xie et al.· 1 citation
The article addresses the problem of automated validation of bibliographic and factual sources in the context of multi-agent intelligent competitive intelligence platforms. The relevance of the study is driven by the growing volume of information flows and the need to ensure the reliability of data used for management decision-making in organizational systems. The aim of the work is to develop the architecture of a validation orchestrator that provides hybrid routing of requests between APIs of scientific databases and web resource parsing modules. A multi-level orchestrator architecture is proposed, including a routing layer, a unified evidence schema, a cross-verification mechanism with a four-level reliability scale (Confirmed, Likely, Unknown, Conflict), and a request audit subsystem. A comparative analysis of API-based and parsing-based approaches is conducted against criteria of stability, source coverage, reproducibility, and completeness of extracted data. The scientific novelty lies in substantiating a hybrid approach to validation with a formalized evidence aggregation schema from heterogeneous sources.
G. Nizhnichenko· Informatization and communic...· 0 citations
In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential. Yet most organizations remain data-rich but insight-poor, constrained by the complexity of querying, interpreting, and explaining enterprise-scale information. We present Polaris, a supervisor-led multi-agent framework for conversational enterprise analytics that bridges this gap. Polaris introduces Dynamic Task Coordination (DTC), a decision-theoretic orchestration layer that models agent-task assignment as adaptive bipartite matching, enabling real-time coordination, recovery, and optimization across specialized agents for querying, visualization, and reasoning. By coupling DTC with reason-first, ReAct-style agents, Polaris transforms natural-language queries into coherent analytical workflows that not only retrieve and visualize data but also explain the underlying"why."Evaluation on structured enterprise datasets demonstrates high semantic fidelity and answer relevancy, underscoring the potential of multi-agent orchestration to deliver trustworthy, end-to-end business intelligence at scale.
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